def __init__(self,earth_structure, namespace):
"""
Class for handling earth models.
:param earth_structure: An EarthStructure JSON dictionary.
"""
add_arguments = namespace['add_arguments']
short_description = namespace.get('short_description',
'No description')
parser = URLArgumentParser(short_description)
add_arguments(parser)
try:
args = parser.parse_params(earth_structure["arguments"])
except SendHelp as helper:
raise SendHelp
self.reflect_file = str(earth_structure["datafile"])
# Load the image data
if earth_structure.get('update_model', None):
response = requests.get(earth_structure["image"])
if os.path.exists(self.reflect_file):
os.remove(self.reflect_file)
image = \
Image.open(StringIO(response.content)).convert("RGB")
image.load()
self.image = np.asarray(image, dtype="int32")
self.units = args.units
self.depth = args.depth
self.reflectivity_method = args.reflectivity_method
self.property_map = {}
# Keep only a direct map for legacy. Input data has name
# attribute we are going to ignore
mapping = earth_structure["mapping"]
# Make a lookup table for vp. This is terribly
# inefficient for memory, but quicker than looping
# dictionaries. There is for sure a better way
self.vp_lookup = np.zeros((256,256,256))
for colour in mapping:
rock = \
rock_properties_type(mapping[colour]["property"])
rgb = colour.split('(')[1].split(')')[0].split(',')
self.vp_lookup[int(rgb[0]), int(rgb[1]),
int(rgb[2])] = rock.vp
self.property_map[colour] = rock
def __init__(self, earth_structure, namespace):
"""
Class for handling earth models.
:param earth_structure: An EarthStructure JSON dictionary.
"""
add_arguments = namespace['add_arguments']
short_description = namespace.get('short_description',
'No description')
parser = URLArgumentParser(short_description)
add_arguments(parser)
try:
args = parser.parse_params(earth_structure["arguments"])
except SendHelp:
raise SendHelp
self.reflect_file = str(earth_structure["datafile"])
self.property_map = {}
# Load the image data
if earth_structure.get('update_model', None):
response = requests.get(earth_structure["image"])
if os.path.exists(self.reflect_file):
os.remove(self.reflect_file)
image = Image.open(StringIO(response.content))\
.convert("RGB")
image.load()
self.image = np.asarray(image, dtype="int32")
self.units = args.units
self.depth = args.depth
self.reflectivity_method = args.reflectivity_method
# Keep only a direct map for legacy. Input data has name
# attribute we are going to ignore
mapping = earth_structure["mapping"]
# Make a lookup table for vp. This is terribly
# inefficient for memory, but quicker than looping
# dictionaries. There is for sure a better way
self.vp_lookup = np.zeros((256, 256, 256))
for colour in mapping:
rock = rock_properties_type(mapping[colour]["property"])
rock.name = mapping[colour]["name"]
rgb = colour.split('(')[1].split(')')[0].split(',')
self.vp_lookup[int(rgb[0]), int(rgb[1]), int(rgb[2])] = rock.vp
self.property_map[colour] = rock
def run_script(args):
matplotlib.interactive(False)
args.reflectivity_method = zoeppritz
args.title = 'Forward model - spatial cross section'
args.wavelet = ricker
args.margin=1
args.slice='spatial'
args.trace = 0
model = urllib2.urlopen(args.model["image"]).read()
model = Image.open(StringIO(model)).convert("RGB")
model = np.asarray(model)
# decimate the first dimension of the model (into sample rate: dt [ms])
ds = float(args.twt_range[1] - args.twt_range[0]) / float(model.shape[0])
x = (np.arange(0, model.shape[0]) * ds) + args.twt_range[0]
f = interp1d(x, model.astype("float"), axis=0, kind="nearest")
xnew = np.arange(args.twt_range[0],args.twt_range[1], dt * 1000.0)
xnew = xnew[np.where(xnew < np.amax(x))]
model_new = f(xnew)
mapping = args.model["mapping"]
for colour in mapping:
rock = rock_properties_type(mapping[colour]["property"])
mapping[colour] = rock
args.ntraces = model_new.shape[1]
return modelr_plot(model_new, mapping, args)
